Artificial neural networks, especially deep neural networks (e.g., artificial neural networks having an input layer, an output layer, and two or more hidden layers of nodes between the input layer and the output layer), are commonly utilized in image processing applications. For example, in an image processing application, a deep neural network may execute image processing steps at each of any number of hidden layers, and may be thereby utilized to perform any number of functions, including image classification, object recognition, image labeling, facial recognition or character or image recognition functions, or others. Depending on their size or depth, artificial neural networks may effectively identify and analyze image content with a level of confidence or accuracy that matches or surpasses those of other known methods.
Artificial neural networks may be reliably utilized in any number of image processing applications. However, performing image processing applications using artificial neural networks may be computationally expensive as compared to other machine learning tools or methods that may be utilized to perform such applications, as artificial neural networks typically consume substantially large portions of available processing power, memory and/or other computing resources. Typically, a level of confidence or accuracy in outputs provided by artificial neural networks performing imaging tasks is a function of the number of calculations performed thereby. An image analysis performed using a traditional deep neural network may require a predetermined number of calculations or computations associated with each of the nodes of each of the hidden layers of the network. Therefore, while networks having larger numbers of hidden layers and nodes may perform intended tasks with a greater degree of accuracy than networks having fewer layers or nodes, more complex networks may require longer periods of time in order to complete such tasks than less complex networks. A decision to use artificial neural networks in processing images commonly includes an inherent choice between accuracy and efficiency, or between greater confidence and fewer computational resources.